The present invention relates to a method of analyzing an image and, more particularly, to a method of characterizing the texture of segments of the image.
In the field of image processing, it is desirable to increase resolution of images (e.g., photographs, thermographs, real time video signals and the like) to facilitate the identification of features therein. Improved image resolution and enhancing images are continuing goals in such areas as robotics, industrial automation and inspection, aerial photography, military surveillance, personal security systems, meteorology, astronomy, biology, medicine, geology and other fields of science and technology.
Three levels of machine vision are generally recognized: low, middle and high. Low level vision generally indicates that processing occurs at the pixel level, such as can be accomplished by low pass filtering, edge detection and the like. A relatively simple mean and standard deviation technique represents one microtexture measure; a moving window pixel pair contrast is another measure; and a first neighbor contrast is another measure known in the art.
Middle level vision usually includes processing methods that extract edges and certain regions. Finally, interpreting total or global scene content in a gestalt approach is generally regarded as high level vision.
When an image is digitized and stored or analyzed as a plurality of picture elements (pixels), the quality of one or more pixels can be modified in accordance with certain arbitrary transformation rules. In this context, pixel quality can represent one or more characteristics including, but not limited to intensity, darkness, color, and tone. The foregoing characteristics, and all combinations thereof, define the microtexture (i.e., the surface roughness) of the image patterns.
An image transformation rule may require a dark pixel to be changed to a light pixel, for example, if it is surrounded by light pixels. Another image transformation rule may require a dark pixel to be changed to a light pixel if any one of its contiguous pixels is light. The foregoing examples are extreme and necessarily simplistic ones, for purposes of illustration. Many other transformation rules can be devised of significantly more sophistication and complexity dependent on internal and external conditions, such as boundary conditions.
Often, the image that results when a given transformation rule is applied is not the final, most enhanced image obtainable. This intermediate or meta-image must then be subjected to the transformation rule again or to another transformation rule to obtain a more enhanced image. The more enhanced image, in turn, may not be the final version and may be operated on by the transformation rule yet again. The cumulative effect of the iterations is a substantially enhanced, altered image, quantifiably different than the raw image on which it was based.
It is not uncommon for a raw image to undergo a number of iterative transformations until (a) a predetermined number of transformation stages has been performed or (b) a desired final image result has been achieved. The time required to process an image using the aforementioned iterative approach is therefore a function of the number of transformation stages required.
One of the most difficult concepts to quantify in image processing is texture. For example, a river in a given aerial photograph has a texture distinct from the texture of a forest in the same photograph. And the texture of a building in the photograph is distinct from either of the foregoing textures. While distinguishing these global features with the use of boundaries and bounded regions based on texture is a relatively simple task for humans, quantifying such macrotexture and characterizing features of a scene by such a quantifying technique has proven to be extremely difficult for machines.
Prior art solutions to image segment identification and characterization problems often require iteration, as mentioned above, whereby a solution is obtained over a period of time while an image is sequentially processed from one stage to another. Eventually, if the processing rule or rules are chosen correctly, the successive images converge into a final, greatly enhanced image. One such method for segmenting texture of an image by means of an iterative process is disclosed in "MITES: A Model-Driven, Iterative Texture Segmentation Algorithm" by L. S. Davis et al., Computer Graphics and Image Processing, Vol. 19, No. 2, pp. 95-110 (Jun. 1982). In addition to shortcomings associated with iterative techniques in general, the MITES system fails to quantify texture.
Unfortunately, the MITES and other techniques are subject to a variety of inconveniences and disadvantages, not the least of which is a great amount of time necessary to complete processing. The nature of serial, iterative activity mandates that work on each succeeding image must await completion of the previous analysis stage. Moreover, errors tend to increase and/or become magnified in iterative processes. Thus, the greater the number of stages required to achieve highly enhanced images, the greater the probability of significant errors perturbing the end result.
A pattern identification technique is illustrated in U.S. Pat. No. 4,617,682, issued to Mori et al on Oct. 14, 1986. In this iterative process, a reference area is measured against any observed area. Both areas are correlated and compared. The result of the comparison, known as the texture component ratio, is merely a measure of the difference between the reference and the examined areas. No features are extracted or analyzed from the reference area. The aforementioned system is therefore nothing more than a correlation between two images at the pixel level. Moreover, because all the values are interdependent, they cannot be parallel processed. In contrast, if the system of the present invention is used for comparing images, such comparison is made at the feature (not the pixel) level. The aforementioned process, therefore, is not only time consuming, but does not provide the feature analysis for either the observed (examined) area or for the reference area itself, as can be achieved with the present invention, explained in greater detail hereinafter.
Another common prior art technique for characterizing or classifying images or portions thereof is a filtering process known as super slicing. By this technique, an image is analyzed pixel by pixel by comparing a predetermined absolute threshold value to each pixel. If the darkness or intensity value of a given pixel is, say, greater than the predetermined value, the pixel is considered black and is treated as such in the course of further arithmetic processing. A characterization of the image is then derived as a function of the number of pixels darker than the threshold value.
This prior art approach to classifying images lacks predictability under all conditions. In the case of images having a preponderance of pixels in a dark range, for example, an arbitrary threshold comparison value could lead to a 100% black characterization. Conversely, an image having mostly light pixels could lead to a 100% white characterization. Neither of these conclusions would be especially useful when two or more similar (but not identical) dark or light images were to be analyzed and classified.
It would be advantageous to provide a system for characterizing image texture by a method that was not necessarily iterative.
It would also be advantageous to provide a system for characterizing image texture that was not prone to iteration-type errors.
It would also be advantageous to provide a system for characterizing image texture that could be performed in a time-saving, parallel manner, such as by parallel processors.
It would also be advantageous to provide a system for processing one image by a plurality of thresholding rules rather than successively processing intermediate images.
It would also be advantageous to provide a system for characterizing image texture in which the system relies on relative or spatial thresholding of each pixel's intensity with respect to neighboring pixels' intensity, rather than characterizing image texture by means of absolute thresholding.
It would be advantageous to provide a system to generate multiple segmentation maps simultaneously using multiple spatial thresholding values.
It would also be advantageous to provide a system for characterizing image texture on the basis of a mesotexture index, which is a function of at least two spatial threshold operations.
It would also be advantageous to quantify portions of an image based on the texture thereof.
It would also be advantageous to provide a system for generating a graphics curve of image texture based on a plurality of mesotexture indices.